Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

Deep learning has achieved tremendous success in recent years. In simple words, deep
learning uses the composition of many nonlinear functions to model the complex …

We study the problem of learning conditional generators from noisy labeled samples, where
the labels are corrupted by random noise. A standard training of conditional GANs will not …

This paper studies the universal approximation property of deep neural networks for
representing probability distributions. Given a target distribution $\pi $ and a source …

Graph embedding has been intensively studied recently, due to the advance of various
neural network models. Theoretical analyses and empirical studies have pushed forward the …

We apply deep-neural-network-based techniques to quantum state classification and
reconstruction. Our methods demonstrate high classification accuracies and reconstruction …

This paper studies the rates of convergence for learning distributions implicitly with the
adversarial framework and Generative Adversarial Networks (GANs), which subsume …

The success of minimax learning problems of generative adversarial networks (GANs) has
been observed to depend on the minimax optimization algorithm used for their training. This …

Over the past few years, there has been growing interest in develo** larger and deeper
neural networks, including deep generative models like generative adversarial networks …

This paper studies how well generative adversarial networks (GANs) learn probability
distributions from finite samples. Our main results establish the convergence rates of GANs …